Identifying microRNAs associated with tumor immunotherapy response using an interpretable machine learning model.

Predicting clinical responses to tumor immunotherapy is essential to reduce side effects and the potential for sustained clinical responses. Nevertheless, preselecting patients who are likely to respond to such treatments remains highly challenging. Here, we explored the potential of microRNAs (miRNAs) as predictors of immune checkpoint blockade responses using a machine learning approach. First, we constructed random forest models to predict the response to tumor ICB therapy using miRNA expression profiles across 19 cancer types. The contribution of individual miRNAs to each prediction process was determined by employing SHapley Additive exPlanations (SHAP) for model interpretation. Remarkably, the predictive performance achieved by using a small number of miRNAs with high feature importance was similar to that achieved by using the entire miRNA set. Additionally, the genes targeted by these miRNAs were closely associated with tumor- and immune-related pathways. In conclusion, this study demonstrates the potential of miRNA expression data for assessing tumor immunotherapy responses. Furthermore, we confirmed the potential of informative miRNAs as biomarkers for the prediction of immunotherapy response, which will advance our understanding of tumor immunotherapy mechanisms.

Pathological Characteristics of Monosodium Iodoacetate-Induced Osteoarthritis in Rats.

BACKGROUND: This study aimed to identify pain-related behavior and pathological characteristics of the knee joint in rats with monosodium iodoacetate (MIA)-induced osteoarthritis (OA). METHODS: Knee joint inflammation was induced by intra-articular injection of MIA (4 mg/50 µL, n = 14) in 6-week-old male rats. Knee joint diameter, weight-bearing percentage on the hind limb during walking, the knee bending score, and paw withdrawal to mechanical stimuli were measured to evaluate edema and pain-related behavior for 28 d after MIA injection. Histological changes in the knee joints were evaluated using safranin O fast green staining on days 1, 3, 5, 7, 14, and 28 after OA induction (n = 3, respectively). Changes in bone structure and bone mineral density (BMD) were examined 14 and 28 d after OA (n = 3, respectively) using micro-computed tomography (CT). RESULTS: The knee joint diameter and knee bending scores of the ipsilateral joint significantly increased 1 d after MIA injection, and the increased knee joint diameter and knee bending score persisted for 28 d. Weight-bearing during walking and paw withdrawal threshold (PWT) decreased from 1 and 5 d, respectively, and were maintained up to 28 d after MIA. Cartilage destruction started on day 1, and Mankin scores for bone destruction significantly increased for 14 d, as shown by micro-CT imaging. CONCLUSION: The present study demonstrated that histopathological structural changes in the knee joint due to inflammation started soon after MIA injection, which induced OA pain from inflammation-related acute pain to spontaneous and evoked associated chronic pain.

Assessment of MicroRNAs Associated with Tumor Purity by Random Forest Regression.

Tumor purity refers to the proportion of tumor cells in tumor tissue samples. This value plays an important role in understanding the mechanisms of the tumor microenvironment. Although various attempts have been made to predict tumor purity, attempts to predict tumor purity using miRNAs are still lacking. We predicted tumor purity using miRNA expression data for 16 TCGA tumor types using random forest regression. In addition, we identified miRNAs with high feature-importance scores and examined the extent of the change in predictive performance using informative miRNAs. The predictive performance obtained using only 10 miRNAs with high feature importance was close to the result obtained using all miRNAs. Furthermore, we also found genes targeted by miRNAs and confirmed that these genes were mainly related to immune and cancer pathways. Therefore, we found that the miRNA expression data could predict tumor purity well, and the results suggested the possibility that 10 miRNAs with high feature importance could be used as potential markers to predict tumor purity and to help improve our understanding of the tumor microenvironment.